Shrinkage and Redundant Feature Elimination Network-Based Robust Image Zero-Watermarking.

To address the contradiction between watermarking robustness and imperceptibility, a zero-watermarking method based on shrinkage and a redundant feature elimination network (SRFENet) is proposed in this paper. First, in order to have the capability of resisting different image attacks, a dense connection was used to extract shallow and deep features from different convolutional layers. Secondly, to reduce unimportant information for robustness and uniqueness, in SRFENet, a shrinkage module was utilized by automatically learning the threshold of each feature channel. Then, to enhance watermarking uniqueness, a redundant feature elimination module was designed to reduce redundant information for the remaining valid features by learning the weights of inter-feature and intra-feature. In order to increase watermarking robustness further, noised images were generated for training. Finally, an extracted feature map from SRFENet was used to construct a zero-watermark. Furthermore, a zero-watermark from the noised image was generated for copyright verification, which is symmetrical to the process of zero-watermark construction from the original image. The experimental results showed that the proposed zero-watermarking method was robust to different single-image attacks (average BER is 0.0218) and hybrid image attacks (average NC is 0.9551), proving the significant generalization ability to resist different attacks. Compared with existing zero-watermarking methods, the proposed method is more robust since it extracts the main image features via learning a large number of different images for zero-watermark construction.